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        <title>Online Deception Detection Refueled by Real World Data Collection</title>
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        <namePart type="given">Zeyu</namePart>
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    <name type="personal">
        <namePart type="given">Ruihong</namePart>
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            <namePart type="given">Ruslan</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high quality deceptive and truthful online reviews1 from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features – advertising speak and writing complexity scores – deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers’ writing styles.</abstract>
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%0 Conference Proceedings
%T Online Deception Detection Refueled by Real World Data Collection
%A Yao, Wenlin
%A Dai, Zeyu
%A Huang, Ruihong
%A Caverlee, James
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F yao-etal-2017-online
%X The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high quality deceptive and truthful online reviews1 from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features – advertising speak and writing complexity scores – deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers’ writing styles.
%R 10.26615/978-954-452-049-6_102
%U https://doi.org/10.26615/978-954-452-049-6_102
%P 793-802

Markdown (Informal)

[Online Deception Detection Refueled by Real World Data Collection](https://doi.org/10.26615/978-954-452-049-6_102) (Yao et al., RANLP 2017)

• Online Deception Detection Refueled by Real World Data Collection (Yao et al., RANLP 2017)

ACL

• Wenlin Yao, Zeyu Dai, Ruihong Huang, and James Caverlee. 2017. Online Deception Detection Refueled by Real World Data Collection. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 793–802, Varna, Bulgaria. INCOMA Ltd..